Tracking active groundwater microbes with D2 O labelling to understand their ecosystem function.

Microbial activity is key in understanding the contribution of microbial communities to ecosystem functions. Metabolic labelling with heavy water (D2 O) leads to the formation of carbon-deuterium bonds in active microorganisms. We illustrated how D2 O labelling allows monitoring of metabolic activity combined with a functional characterization of active populations in complex microbial communities. First, we demonstrated by single cell Raman microspectroscopy that all measured bacterial cells from groundwater isolates growing in complex medium with D2 O were labelled. Next, we conducted a labelling approach with the total groundwater microbiome in D2 O amended microcosms. Deuterium was incorporated in most measured cells, indicating metabolic activity in the oligotrophic groundwater. Moreover, we spiked the groundwater microbiome with organic model compounds. We discovered that heterotrophs assimilating veratric acid, a lignin derivative, showed higher labelling than heterotrophs assimilating methylamine, a degradation product of biomass. This difference can be explained by dilution of the deuterium through hydrogen from the organic compounds. Metaproteomics identified Sphingomonadaceae and Microbacteriaceae as key players in veratric acid degradation, and the metabolic pathways employed. Methylamine, in contrast, stimulated various proteobacterial genera. We propose this combined approach of Raman microspectroscopy and metaproteomics for elucidating the complex metabolic response of microbial populations to different stimuli.

Extended Multiplicative Signal Correction Based Model Transfer for Raman Spectroscopy in Biological Applications.

The chemometric analysis of Raman spectra of biological materials is hampered by spectral variations due to the instrumental setup that overlay the subtle biological changes of interest. Thus, an established statistical model may fail when applied to Raman spectra of samples acquired with a different device. Therefore, model transfer strategies are essential. Herein we report a model transfer approach based on extended multiplicative signal correction (EMSC). As opposed to existing model transfer methods, the EMSC based approach does not require group information on the secondary data sets, thus no extra measurements are required. The proposed model-transfer approach is a preprocessing procedure and can be combined with any method for regression and classification. The performance of EMSC as a model transfer method was demonstrated with a data set of Raman spectra of three Bacillus bacteria spore species ( B. mycoides, B. subtilis, and B. thuringiensis), which were acquired on four Raman spectrometers. A three-group classification by partial least-squares discriminant analysis (PLS-DA) with leave-one-device-out external cross-validation (LODCV) was performed. The mean sensitivities of the prediction on the independent device were considerably improved by the EMSC method. Besides the mean sensitivity, the model transferability was additionally benchmarked by the newly defined numeric markers: (1) relative Pearson's correlation coefficient and (2) relative Fisher's discriminant ratio. We show that these markers have led to consistent conclusions compared to the mean sensitivity of the classification. The advantage of our defined markers is that the evaluation is more effective and objective, because it is independent of the classification models.

Pioneering particle-based strategy for isolating viable bacteria from multipart soil samples compatible with Raman spectroscopy.

The study of edaphic bacteria is of great interest, particularly for evaluating soil remediation and recultivation methods. Therefore, a fast and simple strategy to isolate various bacteria from complex soil samples using poly(ethyleneimine) (PEI)-modified polyethylene particles is introduced. The research focuses on the binding behavior under different conditions, such as the composition, pH value, and ionic strength, of the binding buffer, and is supported by the characterization of the surface properties of particles and bacteria. The results demonstrate that electrostatic forces and hydrophobicity are responsible for the adhesion of target bacteria to the particles. Distinct advantages of the particle-based isolation strategy include simple handling, enrichment efficiency, and the preservation of viable bacteria. The presented isolation method allows a subsequent identification of the bacteria using Raman microspectroscopy in combination with chemometrical methods. This is demonstrated with a dataset of five different bacteria (Escherichia coli, Bacillus subtilis, Pseudomonas fluorescens, Streptomyces tendae, and Streptomyces acidiscabies) which were isolated from spiked soil samples. In total 92% of the Raman spectra could be identified correctly.

Rapid Identification of Pseudomonas spp. via Raman Spectroscopy Using Pyoverdine as Capture Probe.

Pyoverdine is a substance which is excreted by fluorescent pseudomonads in order to scavenge iron from their environment. Due to specific receptors of the bacterial cell wall, the iron loaded pyoverdine molecules are recognized and transported into the cell. This process can be exploited for developing efficient isolation and enrichment strategies for members of the Pseudomonas genus, which are capable of colonizing various environments and also include human pathogens like P. aeruginosa and the less virulent P. fluorescens. A significant advantage over antibody based systems is the fact that siderophores like pyoverdine can be considered as "immutable ligands," since the probability for mutations within the siderophore uptake systems of bacteria is very low. While each species of Pseudomonas usually produces structurally unique pyoverdines, which can be utilized only by the producer strain, cross reactivity does occur. In order to achieve a reliable identification of the captured pathogens, further investigations of the isolated cells are necessary. In this proof of concept study, we combine the advantages of an isolation strategy relying on "immutable ligands" with the high specificity and speed of Raman microspectroscopy. In order to isolate the bacterial cells, pyoverdine was immobilized covalently on planar aluminum chip substrates. After capturing, single cell Raman spectra of the isolated species were acquired. Due to the specific spectroscopic fingerprint of each species, the bacteria can be identified. This approach allows a very rapid detection of potential pathogens, since time-consuming culturing steps are unnecessary. We could prove that pyoverdine based isolation of bacteria is fully Raman compatible and further investigated the capability of this approach by isolating and identifying P. aeruginosa and P. fluorescens from tap water samples, which are both opportunistic pathogens and can pose a threat for immunocompromised patients.

LOC-SERS: A Promising Closed System for the Identification of Mycobacteria.

A closed droplet based lab-on-a-chip (LOC) device has been developed for the differentiation of six species of mycobacteria, i.e., both Mycobacterium tuberculosis complex (MTC) and nontuberculous mycobacteria (NTM), using surface-enhanced Raman spectroscopy (SERS). The combination of a fast and simple bead-beating module for the disruption of the bacterial cell with the LOC-SERS device enables the application of an easy and reliable system for bacteria discrimination. Without extraction or further treatment of the sample, the obtained SERS spectra are dominated by the cell-wall component mycolic acid. For the differentiation, a robust data set was recorded using a droplet based LOC-SERS device. Thus, more than 2100 individual SERS spectra of the bacteria suspension were obtained in 1 h. The differentiation of bacteria using LOC-SERS provides helpful information for physicians to define the conditions for the treatment of individual patients.

Single particle analysis of herpes simplex virus: comparing the dimensions of one and the same virions via atomic force and scanning electron microscopy.

Currently, two types of direct methods to characterize and identify single virions are available: electron microscopy (EM) and scanning probe techniques, especially atomic force microscopy (AFM). AFM in particular provides morphologic information even of the ultrastructure of viral specimens without the need to cultivate the virus and to invasively alter the sample prior to the measurements. Thus, AFM can play a critical role as a frontline method in diagnostic virology. Interestingly, varying morphological parameters for virions of the same type can be found in the literature, depending on whether AFM or EM was employed and according to the respective experimental conditions during the AFM measurements. Here, an inter-methodological proof of principle is presented, in which the same single virions of herpes simplex virus 1 were probed by AFM previously and after they were measured by scanning electron microscopy (SEM). Sophisticated chemometric analyses then allowed a calculation of morphological parameters of the ensemble of single virions and a comparison thereof. A distinct decrease in the virions' dimensions was found during as well as after the SEM analyses and could be attributed to the sample preparation for the SEM measurements. Graphical abstract The herpes simplex virus is investigated with scanning electron and atomic force microscopy in view of varying dimensions.

Magnetic apatite for structural insights on the plasma membrane.

The iron oxide-hydroxyapatite (FeOxHA) nanoparticles reported here differ from those reported before by their advantage of homogeneity and simple preparation; moreover, the presence of carboxymethyldextran (CMD), together with hydroxyapatite (HA), allows access to the cellular membrane, which makes our magnetic apatite unique. These nanoparticles combine magnetic behavior, Raman label ability and the property of interaction with the cellular membrane; they therefore represent an interesting material for structural differentiation of the cell membrane. It was observed by Raman spectroscopy, scanning electron microscopy (SEM) and fluorescence microscopy that FeOxHA adheres to the plasma membrane and does not penetrate the membrane. These insights make the nanoparticles a promising material for magnetic cell sorting, e.g. in microfluidic device applications.

Raman spectroscopic monitoring of the growth of pigmented and non-pigmented mycobacteria.

Raman microspectroscopy has increased in popularity in the field of microbiology because it allows a spectral fingerprinting of bacterial pathogens at an unrivaled speed, which is important for the early treatment of infectious diseases such as tuberculosis. An indispensable prerequisite for the success of this method is a profound knowledge, how the spectral profiles depend on the age of the bacteria. We therefore followed the growth of two rapidly growing Mycobacterium tuberculosis relatives, the pigmented Mycobacterium aurum, and the non-pigmented Mycobacterium smegmatis, by means of Raman microspectroscopy. Both species showed remarkable temporal changes in the single-bacteria Raman spectra: In the signatures of M. aurum, pigment-associated Raman signals could be detected not until 72 h of growth and also remained highly variable thereafter. The Raman spectra of M. smegmatis exhibited lipid signals presumably arising from mycolic acids, which are a hallmark feature of mycobacteria, but only after the bacteria reached the late stationary growth phase (>48 h). A principal component analysis thus classified the Raman spectra according to the cultivation age. In summary, these findings have to be reckoned with in future studies dealing with the identification of mycobacteria via Raman microspectroscopy. Graphical abstract Changes in the chemical composition of bacterial cells over growth time may influence the results of Raman spectroscopic studies of bacteria.

Raman spectroscopic detection and identification of Burkholderia mallei and Burkholderia pseudomallei in feedstuff.

Burkholderia mallei (the etiologic agent of glanders in equines and rarely humans) and Burkholderia pseudomallei, causing melioidosis in humans and animals, are designated category B biothreat agents. The intrinsically high resistance of both agents to many antibiotics, their potential use as bioweapons, and their low infectious dose, necessitate the need for rapid and accurate detection methods. Current methods to identify these organisms may require up to 1 week, as they rely on phenotypic characteristics and an extensive set of biochemical reactions. In this study, Raman microspectroscopy, a cultivation-independent typing technique for single bacterial cells with the potential for being a rapid point-of-care analysis system, is evaluated to identify and differentiate B. mallei and B. pseudomallei within hours. Here, not only broth-cultured microbes but also bacteria isolated out of pelleted animal feedstuff were taken into account. A database of Raman spectra allowed a calculation of classification functions, which were trained to differentiate Raman spectra of not only both pathogens but also of five further Burkholderia spp. and four species of the closely related genus Pseudomonas. The developed two-stage classification system comprising two support vector machine (SVM) classifiers was then challenged by a test set of 11 samples to simulate the case of a real-world-scenario, when "unknown samples" are to be identified. In the end, all test set samples were identified correctly, even if the contained bacterial strains were not incorporated in the database before or were isolated out of animal feedstuff. Specifically, the five test samples bearing B. mallei and B. pseudomallei were correctly identified on species level with accuracies between 93.9 and 98.7%. The sample analysis itself requires no biomass enrichment step prior to the analysis and can be performed under biosafety level 1 (BSL 1) conditions after inactivating the bacteria with formaldehyde.

Quantitative SERS studies by combining LOC-SERS with the standard addition method.

Here, we report on a proof-of-concept study highlighting a new approach for quantitative surface enhanced Raman spectroscopy (SERS) measurements. This has been achieved by implementing the standard addition method (SAM) within a lab-on-a-chip (LOC) system. The approach has been successfully tested to quantify congo red as a model analyte even in the presence of the chemically related molecule methyl red. Thus, the developed concept demonstrates its potential to quantify analytes via SERS in the presence of other SERS active molecules. Graphical Abstract Congo red was quantified by means of the standard addition method implemented in the lab-on-a-chip device. Due to the developed approach, a direct detection out of the sample and in the presence of an interfering substance was possible.

Single virus detection by means of atomic force microscopy in combination with advanced image analysis.

In the present contribution virions of five different virus species, namely Varicella-zoster virus, Porcine teschovirus, Tobacco mosaic virus, Coliphage M13 and Enterobacteria phage PsP3, are investigated using atomic force microscopy (AFM). From the resulting height images quantitative features like maximal height, area and volume of the viruses could be extracted and compared to reference values. Subsequently, these features were accompanied by image moments, which quantify the morphology of the virions. Both types of features could be utilized for an automatic discrimination of the five virus species. The accuracy of this classification model was 96.8%. Thus, a virus detection on a single-particle level using AFM images is possible. Due to the application of advanced image analysis the morphology could be quantified and used for further analysis. Here, an automatic recognition by means of a classification model could be achieved in a reliable and objective manner.

Identification of meat-associated pathogens via Raman microspectroscopy.

The development of fast and reliable sensing techniques to detect food-borne microorganisms is a permanent concern in food industry and health care. For this reason, Raman microspectroscopy was applied to rapidly detect pathogens in meat, which could be a promising supplement to currently established methods. In this context, a spectral database of 19 species of the most important harmful and non-pathogenic bacteria associated with meat and poultry was established. To create a meat-like environment the microbial species were prepared on three different agar types. The whole amount of Raman data was taken as a basis to build up a three level classification model by means of support vector machines. Subsequent to a first classifier that differentiates between Raman spectra of Gram-positive and Gram-negative bacteria, two decision knots regarding bacterial genus and species follow. The different steps of the classification model achieved accuracies in the range of 90.6%-99.5%. This database was then challenged with independently prepared test samples. By doing so, beef and poultry samples were spiked with different pathogens associated with food-borne diseases and then identified. The test samples were correctly assigned to their genus and for the most part down to the species-level i.e. a differentiation from closely-related non-pathogenic members was achieved.

Raman spectroscopy as a potential tool for detection of Brucella spp. in milk.

Detection of Brucella, causing brucellosis, is very challenging, since the applied techniques are mostly time-demanding and not standardized. While the common detection system relies on the cultivation of the bacteria, further classical typing up to the biotype level is mostly based on phenotypic or genotypic characteristics. The results of genotyping do not always fit the existing taxonomy, and misidentifications between genetically closely related genera cannot be avoided. This situation gets even worse, when detection from complex matrices, such as milk, is necessary. For these reasons, the availability of a method that allows early and reliable identification of possible Brucella isolates for both clinical and epidemiological reasons would be extremely useful. We evaluated micro-Raman spectroscopy in combination with chemometric analysis to identify Brucella from agar plates and directly from milk: prior to these studies, the samples were inactivated via formaldehyde treatment to ensure a higher working safety. The single-cell Raman spectra of different Brucella, Escherichia, Ochrobactrum, Pseudomonas, and Yersinia spp. were measured to create two independent databases for detection in media and milk. Identification accuracies of 92% for Brucella from medium and 94% for Brucella from milk were obtained while analyzing the single-cell Raman spectra via support vector machine. Even the identification of the other genera yielded sufficient results, with accuracies of >90%. In summary, micro-Raman spectroscopy is a promising alternative for detecting Brucella. The measurements we performed at the single-cell level thus allow fast identification within a few hours without a demanding process for sample preparation.

Assessment of two isolation techniques for bacteria in milk towards their compatibility with Raman spectroscopy.

The identification of single microorganism in food samples by conventional plating techniques or molecular genetic methods requires a time consuming enrichment step. Raman spectroscopy in combination with a suitable extraction method however offers the possibility to rapidly identify bacteria on a single cell level. Here we evaluate the two well-known bacteria extraction methods from milk: "buoyant density centrifugation" and "enzymatic milk clearing" towards their recovery efficiency and their compatibility with Raman spectroscopy for a rapid identification of microorganisms in milk. The achieved recovery yields are slightly better compared to those which are already applied for food investigations, where a loss of one order of magnitude is usually reached. For example, buoyant density centrifugation allows collecting up to 35% of the milk-spiked microorganisms. To prove the suitability of the isolation techniques for use in combination with the spectroscopic approach, a small Raman database has been created by recording Raman spectra of well-known contaminants in dairy products. Two subspecies of Escherichia coli and three different Pseudomonas species, which were inoculated to UHT (ultra-high-temperature processed) milk and afterwards extracted by the two techniques mentioned above, were analysed. At a first glance, grave spectral artefacts caused by the matrix itself or especially by the extraction techniques were not obvious. But via chemometric analysis, it could be shown that these factors noticeably influence the identification rates: while the samples prepared via milk clearing did not provide sufficient identification results, buoyant density centrifugation allows an identification of the investigated species with an overall accuracy of 91% in combination with linear discriminant analysis.

Analysis of single blood cells for CSF diagnostics via a combination of fluorescence staining and micro-Raman spectroscopy.

This contribution provides a new approach for single blood cell analysis in cerebrospinal fluid (CSF) with the possibility of utilizing simultaneously on the same sample the unique capabilities of the two methods fluorescence staining and Raman spectroscopy. By doing so this technique enables the potential of accurate and rapid cell identification in order to determine cell parameters immediately (e.g. the study of the level of activation or phagocytosis activity of single blood cells). Fluorescence labeling of blood cells offers the great possibility of differentiating easily between the subtypes of white blood cells, while Raman spectroscopy reveals molecular fingerprint information with a spatial resolution down to the diffraction limit. Compared to an unstained cell, the presented results nicely demonstrate that the selected fluorescence dye does not influence the Raman spectrum of a labeled blood cell notably. By the combined application of Raman spectroscopy and statistical data analysis a distinction between white blood cell substructures could be performed. Since several blood cell types also differ in the amount of their cell components, differentiation between several blood cell types is also possible when one blood cell is described in the database by several Raman spectra according their presented sub-microscopic structures. This capability with the possibility of accurate and rapid blood cell identification in cerebrospinal fluid is extremely promising for implementation in clinical diagnostics.

Scents in the stack: olfactometric proficiency testing with an emission simulation apparatus.

Olfactometry is globally acknowledged as a technique to determine odor concentrations, which are used to characterize odors for regulatory purposes, e.g., to protect the general public against harmful effects of air pollution. Although the determination procedure for odor concentrations is standardized in some countries, continued research is required to understand uncertainties of odor monitoring and prediction. In this respect, the present paper strives to provide answers of paramount importance in olfactometry. To do so, a wealth of measurement data originating from six large-scale olfactometric stack emission proficiency tests conducted from 2015 to 2017 was retrospectively analyzed. The tests were hosted at a unique emission simulation apparatus-a replica of an industry chimney with 23 m in height-so that for the first time, conventional proficiency testing (no sampling) with real measurements (no reference concentrations) was combined. Surprisingly, highly variable recovery rates of the odorants were observed-no matter, which of the very different odorants was analyzed. Extended measurement uncertainties with roughly 30-300% up to 20-520% around a single olfactometric measurement value were calculated, which are way beyond the 95% confidence interval given by the widely used standard EN 13725 (45-220%) for assessment and control of odor emissions. Also, no evidence has been found that mixtures of odorants could be determined more precisely than single-component odorants. This is an important argument in the intensely discussed topic, whether n-butanol as current reference substance in olfactometry should be replaced by multi-component odorants. However, based on our data, resorting to an alternative reference substance will not solve the inherent problem of high uncertainty levels in dynamic olfactometry. Finally, robust statistics allowed to calculate reliable odor thresholds, which are an important prerequisite to convert mass concentrations to odor concentrations and vice versa.

Cultivation-Free Raman Spectroscopic Investigations of Bacteria.

Raman spectroscopy is currently advertised as a hot and ambitious technology that has all of the features needed to characterize and identify bacteria. Raman spectroscopy is rapid, easy to use, noninvasive, and it could complement established microbiological and biomolecular methods in the near future. To bring this vision closer to reality, ongoing research is being conducted on spectral fingerprinting. This can yield a wealth of information, from even single bacteria from various habitats which can be further improved by combining Raman spectroscopy with methods such as stable isotope probing to elucidate microbial interactions. In conjunction with extensive statistical analysis, Raman spectroscopy will allow identification of (non)pathogenic bacteria at different taxonomic levels.

Raman spectroscopic identification of Mycobacterium tuberculosis.

In this study, Raman microspectroscopy has been utilized to identify mycobacteria to the species level. Because of the slow growth of mycobacteria, the per se cultivation-independent Raman microspectroscopy emerges as a perfect tool for a rapid on-the-spot mycobacterial diagnostic test. Special focus was laid upon the identification of Mycobacterium tuberculosis complex (MTC) strains, as the main causative agent of pulmonary tuberculosis worldwide, and the differentiation between pathogenic and commensal nontuberculous mycobacteria (NTM). Overall the proposed model considers 26 different mycobacteria species as well as antibiotic susceptible and resistant strains. More than 8800 Raman spectra of single bacterial cells constituted a spectral library, which was the foundation for a two-level classification system including three support vector machines. Our model allowed the discrimination of MTC samples in an independent validation dataset with an accuracy of 94% and could serve as a basis to further improve Raman microscopy as a first-line diagnostic point-of-care tool for the confirmation of tuberculosis disease.

A manual and an automatic TERS based virus discrimination.

Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%.

Direct analysis of clinical relevant single bacterial cells from cerebrospinal fluid during bacterial meningitis by means of micro-Raman spectroscopy.

Bacterial meningitis is a relevant public health concern. Despite the availability of modern treatment strategies it is still a life-threatening disease that causes significant morbidity and mortality. Therefore, an initial treatment approach plays an important role. For in-time identification of specific bacterial pathogens of the cerebrospinal fluid (CSF) and emerged antimicrobial and adjunctive treatment, microbiological examination is of major importance. This contribution spotlights the potential of micro-Raman spectroscopy as a biomedical assay for direct analysis of bacteria in cerebrospinal fluid of patients with bacterial meningitis. The influence of miscellaneous artificial environments on several bacterial species present during bacterial meningitis was studied by means of Raman spectroscopy. The application of chemometric data interpretation via hierarchical cluster analysis (HCA) allows for the differentiation of in vitro cultured bacterial cells and can also be achieved on a single cell level. Moreover as proof of principle the investigation of a CSF sample obtained from a patient with meningococcal meningitis showed that the cerebrospinal fluid matrix does not mask the Raman spectrum of a bacterial cell notably since via chemometric analysis with HCA an identification of N. meningitidis cells from patients with bacterial meningitis could be achieved.